Short answer. A production-grade AI transformation partner gets AI systems past the pilot stage and into live operation inside a bank’s existing core infrastructure, with a named person accountable for each system under DORA and the EU AI Act. An advisor writes the roadmap, a partner ships it into production and stands behind the result.
Most banks and insurers in Central Europe do not lack AI ideas. They lack a way to tell, before signing a contract, which partner will get a use case into production and which one will leave them with a well-documented pilot and nothing running eighteen months later. Gartner estimated that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025, for reasons that are organizational, not technical: poor data quality, unclear business value, and risk controls bolted on too late.
The selection decision is where that outcome gets set. The six criteria below, the build vs buy vs partner framework, and the RFP questions that follow are built for a CIO, CDO, or board member evaluating an AI partner for a regulated financial institution in Czech Republic, Slovakia, or Austria.
Score every shortlisted vendor against all six. A vendor that scores well on one or two and waves at the rest is an advisor with a delivery team attached, not a production partner.
| Criterion | What “good” looks like | Ask in the RFP |
|---|---|---|
| Production track record | A stated share of engagements that reached production, not just a pilot, backed by a number the vendor is willing to have you verify with a real client. | “Of your last 20 AI engagements, how many are running in production today?” |
| Delivery inside a regulated environment | Named projects delivered inside a bank’s or insurer’s existing compliance, audit, and change-management process, not a green-field sandbox built outside it. | “Walk us through one project delivered inside a regulated institution’s change process, from risk sign-off to go-live.” |
| Who signs: accountability under DORA and the AI Act | The partner can name the specific role that owns risk classification, incident reporting, and the register-of-information entry for what they build, not “we follow best practice.” | “Under DORA, who owns the ICT third-party risk entry for this engagement, and who signs the AI Act risk classification?” |
| Integration into legacy core | Evidence of shipping into a core banking or policy administration system that predates the cloud, not only green-field pilots on modern infrastructure. | “Describe an integration into a core system as old as ours. What broke, and how was it fixed?” |
| Cost governance (FinAIOps) | The partner tracks and reports the running cost per model or workflow after go-live, not only a fixed project fee that ends at launch. | “How do you measure and report the ongoing cost of a production AI system twelve months after go-live?” |
| References with a number | A named client willing to take a direct call, attached to one verifiable number, not an anonymized logo wall or a percentage with no source. | “Give us one reference client and one number we can verify with them directly.” |
Before scoring vendors, decide which path the use case actually needs. The three options answer different problems, and picking the wrong one wastes a budget cycle before a single model runs.
| Path | Best when | Main risk |
|---|---|---|
| Build in-house | You already run MLOps and data engineering capability internally, and the use case is core to competitive differentiation. | Commonly 18 to 24 months to a first production system. Specialized AI talent with regulated-domain experience is scarce across CEE. |
| Buy an off-the-shelf AI product | The use case is generic and back-office, and a vendor already has a bank-ready compliance reference for it. | You do not own model behavior. Explaining a specific decision to an auditor under AI Act Article 13 becomes your problem the day it matters, not the vendor’s. |
| Partner with a specialist delivery team | You have the use case and the data. The blocker is the last mile: production hardening, governance wiring, integration into core. | Pick a partner with no production track record and you inherit the same pilot graveyard you were trying to leave. |
In practice, most stalled AI initiatives inside CEE banks and insurers are not stuck on the model. They are stuck on the last mile, which is exactly where the six criteria above earn their place: a partner that scores well there is the one built for the “partner” path.
A production track record beats a client logo wall. Ask for the share of engagements that reached production, not just were piloted.
A common industry rule of thumb for a mid-market bank running a structured RFP is 4 to 8 weeks to select, then a first production result visible within one quarter for a single, well-scoped use case. If a proposed timeline runs past two quarters before anything reaches production, the engagement being sold is advisory, not delivery, whatever the proposal calls it.
Beyond standard commercial terms, an RFP for a regulated financial institution should require a written answer to each of the six criteria above: a production percentage with named clients willing to be contacted, one delivered project inside a regulated change process, the named role that owns DORA and AI Act accountability, one integration into a system as old as yours, a post-go-live cost reporting model, and at least one reference the bank can call without the vendor on the line.
Ableneo shipped 34 production AI projects in 2025, across four countries and seven industries. Four out of five reached production, not a demo. That number, or the equivalent from any shortlisted partner, is what a bank should ask for in writing before signing anything. One example from that portfolio: an AI validation pipeline built for T-Mobile Czech Republic cut fiber consent processing from seven hours to thirty minutes, with over 90% accuracy in live operation. For a longer look at how these systems move from pilot to production under real constraints, see AI in Practice: Lessons from 34 Real-World Implementations in 2025.
Key takeaways
Planning AI in a regulated business? Ableneo takes systems from classification to governed production.